List of Top Generative AI Frameworks and Tools for 2025

In recent times, the chances are that you’ve had some encounter with generative AI. The most probable example is the groundbreaking chatGPT that was launched in November 2021. Since its launch, students have been prompting the tool for assignment solutions while others generate humanlike images, error-free content, and even lines of code with it. Other popular examples you may know include Gemini, Perplexity, and DALL-E. However, while these amazing pieces of technology are truly outstanding, they can only function with the help of special tools and structured development environments. These special tools are known as Generative AI frameworks.

Think of it as the set of tools used to develop and deploy generative AI tools like chatGPT. 

If you’re fascinated by this type of stuff and would like to develop your own version of chatGPT, this article is the perfect place to start. You’ll get a foundational understanding of generative AI, the most common frameworks to build your own tools, and some important criteria to consider when selecting the perfect tool for you.

What is Generative AI?

Generative AI is a type of supervised or semi-supervised artificial intelligence technology built to create different types of content, such as text, video, images, and audio, by leveraging information from a vast amount of data. This elite technology is not just a set of algorithms but a force of creativity that runs on the volume of online data. With the right prompt,  GenAI can create near-perfect poetry, crystal-clear imageries, melodic songs, and even intricate lines of code in some cases. 

This amazing piece of technology is able to produce these creative outputs because it has been trained with so much data using complex mathematics and substantial computing power. As a result, the AI can identify patterns in a complex dataset, predict outcomes, and consequently produce awesome content outputs based on those predictions. 

So, it is safe to say Generative AI refers to a smart technology that produces awesome content outputs based on the information it has learned from a vast amount of training data. It is a specific type of AI system that learns to produce content outputs that resemble the one it has been trained on. 

Top 5 Generative AI Frameworks and Tools for Beginners

Whether you’re an artificial intelligence or machine learning developer or engineer, the following are some of the best frameworks you can use to create generative AI models.

1. LangChain

LangChain is an open-source generative AI framework that was built by Harrison Chase in October 2022. The framework helps data engineers develop applications like chatGPT, text summarization tools, automated question-answering systems, and other tailor-made tools that are powered by large language models (LLMs) via a simpler and more comprehensible approach. 

Here’s how it works. 

Remember we mentioned earlier that Generative AI models are able to produce content output by learning from similar content? 

Well, LangChain facilitates that learning process by breaking down large documents into smaller chunks that are then transformed into vectors. It then stores these vectors in a vector database so that it can retrieve them quickly when it needs that information. So, anytime a user prompts the AI, LangChain searches for the relevant information by querying the vector store. Furthermore, the LLM interprets the prompt’s context and tailors its response using the interpreted context and the information retrieved from its vector stores. 

But how does LangChain do this? This generative AI framework helps developers create awesome generative AI tools due to the following core components and features: 

• LLM Interface: LangChain makes it easy for developers to choose the LLM models for their applications because it has APIs supporting various LLMs. Whether it’s OpenAI’s GPT, Cohere, or Hugging Face Models, they can connect easily by making simple AI calls without having to write complex code. 
• Agent Modules: This designs a series of steps for LLMs to help them solve complex problems when users input a prompt. 
• Data Connection and Retrieval Modules: As we highlighted earlier, LangChain has several modules that make it easy to transform, store, search, and retrieve information from databases. 
• Data Augmented Generation: This feature combines LLMs with external data sources in order to enhance the relevance and accuracy of the generated content outputs.
• Agents: LangChain provides developers with special chains, tools, and libraries that help the language model decide the best sequence in response to a query. 

Pretty cool, right? 

Using this same operational procedure, developers and data engineers can use LangChain to build different types of Generative AI tools. Some of them include the following: 

• Customer service chatbots that provide contextual responses to customer inquiries
• Virtual assistants that provide educative learning materials to students
• Interactive applications for games and other entertainment
• Tools for generating articles, product descriptions, and summaries based on external data

2. SingleStore Notebook

SingleStore Notebook is a cutting-edge generative AI framework developed to help data engineers and scientists analyze and explore data better, especially when working with SingleStore’s distributed SQL database. Based on the popular Jupyter Network, it creates a familiar environment for developers, making it easy for them to implement complex data analysis, analytics, and machine learning processes whenever they’re building GenAI tools. 

The SingleStore Notebook is able to create this familiar environment with the help of the following core features: 

• Native SingleStore SQL Support: With the SingleStore Notebook, developers and data engineers do not need complex connection strings whenever they need information from the SingleStore database. They can just query the database directly. 
• SQL/Python Interoperability: Developers can execute SQL commands and manipulate results in Python data frames effortlessly. 
• Interactive Data Visualization: Notebooks support popular data visualization libraries like Matplotlip and Plotly, thus making it easy for users to create interactive charts and graphs directly within notebooks. 
• Collaborative Workflows: Data engineers and developers can work jointly using Notebooks for better and more comprehensive data analysis and exploration. 

SingleStore Notebooks applies these features, and more to help data professionals generate insights and visualizations based on real-time data without leaving the Notebook environment. Its ease of use and collaborative features make it applicable for creating intelligent AI applications, building data engineering pipelines, and conducting complex data analysis using the private data in the SingleStoreDB. 

3. TensorFlow

TensorFlow is an end-to-end Generative AI framework platform designed to help both beginners and experts develop and deploy machine learning (ML) models effectively. The platform has an entire ecosystem that supports the development of machine-learning models that solve challenging real-world problems. Furthermore, it supports various levels of abstraction, thus allowing developers to tailor their approach to their unique needs. 

Highly reputable companies like Airbnb, Coca-Cola, and many more are using TensorFlow for training their AI models and even building full-fledged AI tools due to the following features: 

• Keras functional API: This is a powerful tool that helps developers to build models that are more flexible than the Keras.sequential API
• Data Pipeline Management: TensorFlow offers several tools for ensuring high-quality input data, from scalable data pipelines to preprocessing layers and validation tools. These tools improve the accuracy of the ML models. 
• Robust Ecosystem: The platform has an extensive library of powerful add-on tools, libraries, and models for developers to experiment with. Some of these include TensorFlow Probability and Tensor2Tensor, both of which make TensorFlow more functional for probabilistic modeling and sequence generation. 
• Distributed Training Capabilities: TensorFlow uses the distribution strategy API to enable efficient training across different hardware configurations without altering model definitions. 

These TensorFlow features make it widely applicable  in the following use scenarios: 

• Image Classification and Detection: TensorFlow can be used to detect objects and classify images at scale to improve user experiences. Airbnb’s engineering and data science team already uses it to improve the experiences of its guests. 
• Information Extraction: AirBus uses TensorFlow to monitor changes to the earth’s surface by extracting information from satellite images. It also analyzes this information and delivers valuable insights to clients. 
• Recommendation Systems and Image Search: TensorFlow helps to build machine learning models that possess deep image and natural language understanding features. These features improve customer experiences by helping users discover more relevant listings and find images faster.
• Machine Learning Implementations: TensorFlow has awesome products like TensorFlow Lite that can help businesses power ML implemenetations. For instance, Google  uses TensorFlow Lite for this purpose in products like Search, Gmail, and Translate. 
• Predictive Analytics: In fields like finance and healthcare, TensorFlow can be used to make forecasts based on historical data. 

Whether you’re a novice seeking simplicity or an experienced developer seeking depth in machine learning capabilities, TensorFlow is a powerful framework with extensive features that can help you handle complex AI projects across diverse applications. 

4. LlamaIndex

LlamaIndex is a simple, flexible, generative AI framework for building agentic generative AI applications. These applications often use large language models (LLMs) to work with an organization’s enterprise data, to break down complex questions, plan out tasks and call out APIs. This open-source data orchestration framework enables AI engineers to build knowledge assistants using LLMs connected to an organization’s enterprise data. 

Imagine if an organization has a huge library of information which includes spreadsheets, PDFs and databases. This information library generally contains answers to whatever query a customer might have about the business. However, it is scattered and hard to access for them. LlamaIndex acts as a librarian by organizing this information so that users can easily find and utilize the information via AI agents. 

Its end goal is similar to that of the other generative AI frameworks on this list. However, it executes its functions with the help of the following unique features: 

• Data Connectors (LlamaHub): AI engineers are able to connect data from various sources and in different formats like APIs, PDFs, SQL, and NoSQL systems due to LlamaHub’s comprehensive data integration capabilities. 
• Document Operations: LlamaIndex can insert, update, delete, and refresh document indexes to maintain current datasets. 
• Data Synthesis: The index produces coherent responses by synthesizing insights from multiple documents or different sources. 
• Integration Options: You can connect your GenAI application to a wide range of vector sources, large language models (LLMs), and other data sources, making it more versatile and highly compatible. 
• Query Engine Router: LlamaIndex gives your applications the option to select between different query engines that match a query’s context so that the responses are better. 

Based on these features, LlamaIndex can be particularly effective in the development of the following kinds of AI tools: 

• Chatbots: Businesses can use LlamaIndex to create chatbots that interact with customers in real time using the business’s information. 
• Question Answering Systems: Applications capable of responding to natural language queries. 
• Data Enhancements: For specific business or application needs, LlamaIndex can combine public and private data for a more comprehensive data reservoir. 
• Data Interactions: It helps to work with a large volume of unstructured data seamlessly. 

Developers and AI engineers who want to do awesome things with generative AI by integrating custom enterprise data with large language models have found the perfect framework in LlamaIndex. Its robust features facilitate efficient data management, retrieval, and application development across various domains. 

5. Hugging Face

Hugging Face is a no-code and low-code platform that provides ready-made tools (AI models) for training, deploying, and publishing state-of-the-art generative AI tools. As a beginner, it makes it easy for you to use advanced AI models, especially for tasks involving language, like chatbots and text generation. More importantly, it helps you build your own smart applications without having to spend time learning all the complicated details associated with generative AI development services and large language models. 

Furthermore, its recent integration into the AWS marketplace makes it easy for AWS users to streamline their workflows by connecting their AWS accounts directly to Hugging Face services. 

Building a generative AI tool with Hugging Face is pretty straightforward. You simply need to choose the one you prefer from a vast library of pre-trained AI models. And from then on, Hugging Face handles the rest. The platform scales the service based on demand and ensures that your data is secure. Furthermore, the entire deployment process is streamlined with the use of interference endpoints that optimize your resources using cost-effective strategies like scale-to-zero capabilities. 

So, essentially, you can build and launch your own generative AI tool with Hugging Face in just a few clicks without having to do any serious coding. 

Hugging Face makes this happen with the use of the following features: 

• Comprehensive Model Hub: With an extensive repository of pre-trained models across various tasks such as Transformers and Sentence Transformers, Hugging Face provides quick access to cutting-edge solutions that do not need retraining.
• Security Compliance: Hugging Face has essential security features, such as SOC2 Type 2 Certifications and GDPR compliance. These ensure that the tools you build meet stringent data protection standards. 
• Inference Endpoints: These endpoints ensure your generative AI models are production-ready APIs that are ready to go. 
• Integrated Billing With AWS: Users can manage payments for Hugging Face services directly through their AWS accounts. 

Like most of the other frameworks discussed earlier, Hugging Face can be used to develop and deploy generative chatbots, automated content creation tools, and data science workflows. 

Choosing a Generative AI Framework as a Beginner: Top Criteria to Consider

The frameworks above are some of the most popular options, especially for beginners. Each has unique features, advantages, and disadvantages that determine how smoothly and effectively you can build your generative AI tool. 

However, if you’re stuck between multiple choices, certain criteria can help you make the better choice for you. The following are some of them: 

1. Efficiency and Scalability

Depending on your unique query output requirements, you need to confirm the volume of datasets your generative AI framework can handle. More importantly, you need to monitor the quality of the results it produces as it processes more datasets. You want to go for frameworks that can process more data without losing efficiency. 

2. Ease of Use

While some frameworks are low or no-code solutions that help you build GenAI tools in a few clicks, others require extensive developer experience. Depending on your team’s skillset, you need to choose solutions that match your level of expertise. 

Another factor that determines a framework’s ease of use is the availability of comprehensive documentation and community support. It is important to use an AI framework that your AI engineers and developers can use easily with minimal effort, and these criteria are some major pointers that determine that. 

3. Ethical Considerations

It is becoming more and more apparent that AI systems have immense capabilities with daily advancements. As a result, multiple concerns have been raised about the possibilities of bias, privacy invasions, and other negative implications of AI systems. 

That is why you must ensure any framework you choose abides by stringent ethical standards that ensure the AI tools you create promote good practices that mitigate these negative possibilities. 

4. Versatility

Your concern here should be: what generative AI models does the framework support? 

Whether your tool is for generative texts, videos, images, or even code snippets, you need a framework that can handle different tasks, perform across several domains, and integrate with different data sources. 

Conclusion

Without any doubt, generative AI development is booming, with most businesses and organizations integrating this elite technology into their products. However, to build these awesome products, they’re relying on awesome frameworks like LlamaIndex, TensorFlow, Hugging Face, and many others. 

While all these options are great in their own way, some are better suited for the development of certain types of generative AI tools. That’s why you need to evaluate them based on certain key criteria. 

Efficiency and scalability make it easy for you to handle large datasets without sacrificing quality, while ease of use determines how quickly you can start. Furthermore, ethical considerations ensure your tools promote responsible practices. Finally, versatility will enhance your framework’s utility across different projects. 

Everything matters, and carefully selecting the right frameworks for your needs equips you to truly enjoy the benefits of generative AI effectively. 

Frequently Asked Questions